Strategies to achieve population control of HCV infection: results of a multidisciplinary focus group
Bibliographic record
Abstract
Background: To meet the World Health Organization's ambitious target to eradicate hepatitis C virus (HCV) by 2030, a comprehensive strategy is needed in Canada to ensure everyone infected with HCV is identified, diagnosed, and treated. The purpose of this study was to highlight the barriers to any strategy aimed at achieving this goal. Methods: = 11) that consisted of clinicians, patients, drug program budget managers, industry representatives, and individuals from provincial public health and federal agencies in Canada. The group met in person for a half-day focus group session. Two discussions were held: one on future barriers related to HCV treatment and one related to HCV screening. A grounded theory approach was used to elicit key themes from the day's discussion. Results: Nine themes were identified. Four themes related to HCV screening: public awareness and engagement, resource infrastructure and capacity, heterogeneity between provinces, and mechanisms of screening. Three themes related to HCV treatment: access to treatment and illicit drug use, linkage to care, and predicting post-treatment outcomes. Two overarching themes that contributed to most discussions were a focus on baby boomers versus persons who inject drugs and the need for further education and training. Conclusion: The views and findings extracted from this qualitative research complement proposals of national strategies from organizations such as the Canadian Network on Hepatitis C. This work highlights the financial, logistical, and ethical constraints that need to be tackled to make HCV elimination proposals a reality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".